library(tidyverse)
library(readxl)
library(janitor)
library(here)
library(lubridate)
library(plotly)
library(mosaic)
library(datapasta)
library(patchwork)
# loading in data
meteorological_data <- read_xlsx(here("data", "Sensor Data", "Meteorological Data.xlsx"))
sensor_data <- read_xlsx(here("data", "Sensor Data", "Sensor Data.xlsx"))
lekagul_sensor_data <- read_csv(here("data", "Traffic Data", "Lekagul Sensor Data.csv"))
# basic data cleaning
meteorological_data <- meteorological_data %>%
clean_names() %>%
select(-x4)
sensor_data <- sensor_data %>%
clean_names() %>%
mutate(monitor = as.factor(monitor))
lekagul_sensor_data <- lekagul_sensor_data %>%
clean_names() %>%
mutate(car_type = as.factor(car_type))
For Data Challenge 3 we were asked as experts in visual analytics to help Mitch Vogel analyze these datasets since he has been discovering signs that the number of nesting pairs of the Rose-Crested Blue Pipit is decreasing. Something is suspicious, but what?
knitr::include_graphics("https://www.allaboutbirds.org/guide/assets/photo/297326811-1280px.jpg")
What does the Traffic Data tell us?
“Patterns of Life” analyses depend on recognizing repeating patterns of activities by individuals or groups. Describe some of the daily patterns of life you observe in the vehicles traveling through and within the park. Characterize the patterns by describing:
the kinds of vehicles
their spatial activities (where do they go?)
their temporal activities (when does the pattern happen?)
and provide a hypothesis of what the pattern represents (for example, if I drove to a coffee house every morning, but did not stay for long, you might hypothesize I’m getting coffee “to-go”).
Some patterns may appear over longer periods of time (in this case, over multiple days). Describe a few patterns of life that occur over longer time periods by vehicles traveling through and within the park. You may want to use the same what-where-when breakdown described above to frame your description.
Some activities may deviate from an established pattern or are just difficult to explain from what you know of a situation. Describe any unusual patterns (either single day or multiple days) and highlight why you find them unusual.
What are the top 3 patterns you discovered that you suspect could be most impactful to bird life in the preserve?
What does the Sensor Data tell us?
Turning your attention to the sensor data, characterize the sensors’ performance and operation. Are they all working properly?
Can you detect any unexpected behaviors of the sensors by analyzing the readings they capture?
What about the chemicals did we find?
Which chemicals are being detected by the sensor group?
p1 <- sensor_data %>%
group_by(chemical) %>%
ggplot(aes(x = date_time, y = reading, color = chemical)) +
geom_line(aes(group = chemical), alpha = 0.4) +
labs(title = "Sensor Readings by Chemical Type", x = "Month", y = "Monitor Reading")
ggplotly(p1)